
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


df = pd.read_csv('/content/Employee-Attrition.csv')

df


df.head()


df.tail()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1470 entries, 0 to 1469
Data columns (total 35 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Age                       1470 non-null   int64 
 1   Attrition                 1470 non-null   object
 2   BusinessTravel            1470 non-null   object
 3   DailyRate                 1470 non-null   int64 
 4   Department                1470 non-null   object
 5   DistanceFromHome          1470 non-null   int64 
 6   Education                 1470 non-null   int64 
 7   EducationField            1470 non-null   object
 8   EmployeeCount             1470 non-null   int64 
 9   EmployeeNumber            1470 non-null   int64 
 10  EnvironmentSatisfaction   1470 non-null   int64 
 11  Gender                    1470 non-null   object
 12  HourlyRate                1470 non-null   int64 
 13  JobInvolvement            1470 non-null   int64 
 14  JobLevel                  1470 non-null   int64 
 15  JobRole                   1470 non-null   object
 16  JobSatisfaction           1470 non-null   int64 
 17  MaritalStatus             1470 non-null   object
 18  MonthlyIncome             1470 non-null   int64 
 19  MonthlyRate               1470 non-null   int64 
 20  NumCompaniesWorked        1470 non-null   int64 
 21  Over18                    1470 non-null   object
 22  OverTime                  1470 non-null   object
 23  PercentSalaryHike         1470 non-null   int64 
 24  PerformanceRating         1470 non-null   int64 
 25  RelationshipSatisfaction  1470 non-null   int64 
 26  StandardHours             1470 non-null   int64 
 27  StockOptionLevel          1470 non-null   int64 
 28  TotalWorkingYears         1470 non-null   int64 
 29  TrainingTimesLastYear     1470 non-null   int64 
 30  WorkLifeBalance           1470 non-null   int64 
 31  YearsAtCompany            1470 non-null   int64 
 32  YearsInCurrentRole        1470 non-null   int64 
 33  YearsSinceLastPromotion   1470 non-null   int64 
 34  YearsWithCurrManager      1470 non-null   int64 
dtypes: int64(26), object(9)
memory usage: 402.1+ KB


df.describe()


corr = df.corr()
corr

<ipython-input-168-4381f08f6434>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  corr = df.corr()


df.Attrition.value_counts()

No     1233
Yes     237
Name: Attrition, dtype: int64


df.BusinessTravel.value_counts()

Travel_Rarely        1043
Travel_Frequently     277
Non-Travel            150
Name: BusinessTravel, dtype: int64


df.Department.value_counts()

Research & Development    961
Sales                     446
Human Resources            63
Name: Department, dtype: int64


df.Education.value_counts()

3    572
4    398
2    282
1    170
5     48
Name: Education, dtype: int64


df.EducationField.value_counts()

Life Sciences       606
Medical             464
Marketing           159
Technical Degree    132
Other                82
Human Resources      27
Name: EducationField, dtype: int64


df.EmployeeCount.value_counts()

1    1470
Name: EmployeeCount, dtype: int64


df.isnull().any()

Age                         False
Attrition                   False
BusinessTravel              False
DailyRate                   False
Department                  False
DistanceFromHome            False
Education                   False
EducationField              False
EmployeeCount               False
EmployeeNumber              False
EnvironmentSatisfaction     False
Gender                      False
HourlyRate                  False
JobInvolvement              False
JobLevel                    False
JobRole                     False
JobSatisfaction             False
MaritalStatus               False
MonthlyIncome               False
MonthlyRate                 False
NumCompaniesWorked          False
Over18                      False
OverTime                    False
PercentSalaryHike           False
PerformanceRating           False
RelationshipSatisfaction    False
StandardHours               False
StockOptionLevel            False
TotalWorkingYears           False
TrainingTimesLastYear       False
WorkLifeBalance             False
YearsAtCompany              False
YearsInCurrentRole          False
YearsSinceLastPromotion     False
YearsWithCurrManager        False
dtype: bool


df.isnull().sum()

Age                         0
Attrition                   0
BusinessTravel              0
DailyRate                   0
Department                  0
DistanceFromHome            0
Education                   0
EducationField              0
EmployeeCount               0
EmployeeNumber              0
EnvironmentSatisfaction     0
Gender                      0
HourlyRate                  0
JobInvolvement              0
JobLevel                    0
JobRole                     0
JobSatisfaction             0
MaritalStatus               0
MonthlyIncome               0
MonthlyRate                 0
NumCompaniesWorked          0
Over18                      0
OverTime                    0
PercentSalaryHike           0
PerformanceRating           0
RelationshipSatisfaction    0
StandardHours               0
StockOptionLevel            0
TotalWorkingYears           0
TrainingTimesLastYear       0
WorkLifeBalance             0
YearsAtCompany              0
YearsInCurrentRole          0
YearsSinceLastPromotion     0
YearsWithCurrManager        0
dtype: int64


# As there are no null values we can continue with Data Visualization.
sns.pairplot(df)
plt.figure(figsize=(20, 15))
plt.show()

<Figure size 2000x1500 with 0 Axes>


plt.subplots(figsize=(20,15))
sns.heatmap(corr,annot=True)

<Axes: >


plt.figure(figsize=(8, 6))
sns.countplot(data=df, x='Attrition')
plt.title('Attrition Count')
plt.xlabel('Attrition')
plt.ylabel('Count')
plt.show()


plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='Department', y='MonthlyIncome', hue='Attrition')
plt.title('Monthly Income by Department and Attrition')
plt.xlabel('Department')
plt.ylabel('Monthly Income')
plt.xticks(rotation=45)
plt.show()


plt.figure(figsize=(12, 6))
sns.countplot(data=df, x='JobRole', hue='Attrition')
plt.title('Attrition by Job Role')
plt.xlabel('Job Role')
plt.ylabel('Count')
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize=(8, 6))
sns.histplot(data=df, x='TotalWorkingYears', kde=True)
plt.title('Distribution of Total Working Years')
plt.xlabel('Total Working Years')
plt.ylabel('Frequency')
plt.show()


plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='JobSatisfaction', y='YearsInCurrentRole', hue='Attrition')
plt.title('Years in Current Role by Job Satisfaction and Attrition')
plt.xlabel('Job Satisfaction')
plt.ylabel('Years in Current Role')
plt.show()


plt.figure(figsize=(8, 6))
sns.scatterplot(data=df, x='YearsInCurrentRole', y='YearsAtCompany', hue='Attrition')
plt.title('Years in Current Role vs. Years at Company')
plt.xlabel('Years in Current Role')
plt.ylabel('Years at Company')
plt.show()


plt.figure(figsize=(8, 6))
sns.swarmplot(data=df, x='Attrition', y='DailyRate')
plt.title('Daily Rate vs. Attrition')
plt.xlabel('Attrition')
plt.ylabel('Daily Rate')
plt.show()


business_travel_counts = df['BusinessTravel'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(business_travel_counts, labels=business_travel_counts.index, autopct='%1.1f%%', startangle=90)
plt.title('Business Travel Distribution')
plt.show()


plt.figure(figsize=(10, 6))
sns.lineplot(data=df, x='Age', y='JobSatisfaction', hue='Attrition', ci=None)
plt.title('Job Satisfaction vs. Age')
plt.xlabel('Age')
plt.ylabel('Job Satisfaction')
plt.show()

<ipython-input-187-587676ac6f43>:2: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.lineplot(data=df, x='Age', y='JobSatisfaction', hue='Attrition', ci=None)


plt.figure(figsize=(10, 6))
sns.violinplot(data=df, x='MaritalStatus', y='TrainingTimesLastYear', hue='Attrition', split=True)
plt.title('Training Times Last Year by Marital Status and Attrition')
plt.xlabel('Marital Status')
plt.ylabel('Training Times Last Year')
plt.xticks(rotation=45)
plt.show()


plt.figure(figsize=(8, 6))
sns.scatterplot(data=df, x='YearsWithCurrManager', y='YearsAtCompany', hue='Attrition')
plt.title('Years With Current Manager vs. Years at Company')
plt.xlabel('Years With Current Manager')
plt.ylabel('Years at Company')
plt.show()


plt.figure(figsize=(10, 6))
sns.boxplot(data=df, x='Gender', y='YearsSinceLastPromotion', hue='Attrition')
plt.title('Years Since Last Promotion by Gender and Attrition')
plt.xlabel('Gender')
plt.ylabel('Years Since Last Promotion')
plt.show()


overtime_counts = df['OverTime'].value_counts()
plt.figure(figsize=(8, 8))
plt.pie(overtime_counts, labels=overtime_counts.index, autopct='%1.1f%%', startangle=90)
plt.title('OverTime Distribution')
plt.show()


plt.figure(figsize=(10, 6))
sns.lineplot(data=df, x='Age', y='MonthlyIncome', hue='Attrition', ci=None)
plt.title('Monthly Income vs. Age')
plt.xlabel('Age')
plt.ylabel('Monthly Income')
plt.show()

<ipython-input-192-be4c585955f0>:2: FutureWarning: 

The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.

  sns.lineplot(data=df, x='Age', y='MonthlyIncome', hue='Attrition', ci=None)


# Label Encoding
from sklearn.preprocessing import LabelEncoder
ordinal_categorical_cols = ['BusinessTravel', 'Department', 'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'Over18', 'OverTime','Attrition','Education', 'EnvironmentSatisfaction', 'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel', 'WorkLifeBalance']
df_encoded = df.copy()
label_encoder = LabelEncoder()
for col in ordinal_categorical_cols:
    df_encoded[col] = label_encoder.fit_transform(df_encoded[col])


df_encoded


# Outlier Detection & Removal using Z-Score Test.
from scipy import stats
columns_to_check = [
    'Age', 'DailyRate', 'MonthlyIncome', 'DistanceFromHome',
    'YearsAtCompany', 'HourlyRate', 'PercentSalaryHike',
    'TotalWorkingYears', 'TrainingTimesLastYear',
    'YearsInCurrentRole', 'YearsSinceLastPromotion', 'YearsWithCurrManager'
]
def remove_outliers_zscore(df, columns, z_threshold=3):
    df_no_outliers = df.copy()
    for column in columns:
        z_scores = np.abs(stats.zscore(df_no_outliers[column]))
        df_no_outliers = df_no_outliers[(z_scores < z_threshold)]
    return df_no_outliers
cleaned_data = remove_outliers_zscore(df_encoded, columns_to_check)
num_removed_outliers = len(df_encoded) - len(cleaned_data)
print(f"Number of outliers removed: {num_removed_outliers}")
print(f"New dataset size after removing outliers: {cleaned_data.shape}")

Number of outliers removed: 107
New dataset size after removing outliers: (1363, 35)


# Cleaned encoded data set with no outliers.
cleaned_data


# Splitting Dependent and Independent variables
target_column = 'Attrition'
y = cleaned_data[target_column]
X = cleaned_data.drop(columns=[target_column])


X.head()


y.head()

0    1
1    0
2    1
3    0
4    0
Name: Attrition, dtype: int64


cleaned_data.shape

(1363, 35)


X.shape

(1363, 34)


# Splitting Data into Train and Test Set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print("X_train shape:", X_train.shape)
print("X_test shape:", X_test.shape)
print("y_train shape:", y_train.shape)
print("y_test shape:", y_test.shape)

X_train shape: (1090, 34)
X_test shape: (273, 34)
y_train shape: (1090,)
y_test shape: (273,)


X_train.head()


X_test.head()


y_train.head()

1085    1
1393    0
911     1
1027    0
56      0
Name: Attrition, dtype: int64


y_test.head()

460     0
1006    1
843     0
486     0
461     0
Name: Attrition, dtype: int64


# Feature Scaling - Standardization (Z-score Scaling)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_standardized = scaler.fit_transform(X_train)
X_test_standardized = scaler.transform(X_test)


X_train_standardized

array([[-0.57497515, -0.93717743, -0.60471296, ..., -0.56597211,
         2.40499708,  1.00332169],
       [-1.03218684,  0.58294667,  0.37446629, ...,  0.97630188,
        -0.71630378,  1.00332169],
       [-1.26079268, -0.93717743, -0.51003405, ..., -1.1828817 ,
        -0.27040365, -1.1616513 ],
       ...,
       [-0.57497515, -0.93717743, -0.12384376, ...,  0.97630188,
        -0.71630378,  1.31260354],
       [ 1.71108327, -0.93717743,  0.40934799, ..., -1.1828817 ,
         2.40499708,  1.00332169],
       [-0.11776347, -0.93717743, -1.63870631, ...,  0.05093749,
        -0.27040365,  0.07547612]])


X_test_standardized

array([[-1.14648976,  0.58294667, -0.07152121, ..., -1.1828817 ,
        -0.71630378, -1.1616513 ],
       [ 1.48247743, -0.93717743,  1.67256396, ..., -0.25751731,
        -0.27040365, -0.23380573],
       [-1.14648976,  0.58294667,  1.44583289, ...,  0.97630188,
        -0.71630378,  1.00332169],
       ...,
       [ 1.13956867,  0.58294667, -0.78659613, ...,  0.97630188,
         2.40499708,  1.00332169],
       [-1.60370145,  0.58294667, -1.37211043, ..., -0.8744269 ,
        -0.27040365, -0.54308759],
       [ 2.16829496,  0.58294667,  1.58785125, ..., -0.56597211,
        -0.27040365, -0.54308759]])


plt.figure(figsize=(15, 10))
sns.heatmap(X_train_standardized, cmap='coolwarm', cbar=True, annot=False)
plt.title('Scaled Training Dataset')
plt.xlabel('Feature Index')
plt.ylabel('Sample Index')
plt.show()


# Logistic Regression Model
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score,roc_curve
logistic_regression_model = LogisticRegression(random_state=42)
logistic_regression_model.fit(X_train_standardized, y_train)
y_pred_logistic_regression = logistic_regression_model.predict(X_test_standardized)
accuracy_lr = accuracy_score(y_test, y_pred_logistic_regression)
print("Logistic Regression Accuracy:", accuracy_lr)
print("Classification Report:\n", classification_report(y_test, y_pred_logistic_regression))

Logistic Regression Accuracy: 0.8754578754578755
Classification Report:
               precision    recall  f1-score   support

           0       0.89      0.97      0.93       229
           1       0.71      0.39      0.50        44

    accuracy                           0.88       273
   macro avg       0.80      0.68      0.71       273
weighted avg       0.86      0.88      0.86       273


probability = logistic_regression_model.predict_proba(X_test_standardized)[:,1]


# Roc_Curve
fpr,tpr,threshsholds = roc_curve(y_test,probability)


probability

array([2.28752232e-01, 4.41662043e-01, 1.15628793e-02, 1.71529396e-01,
       6.12069188e-02, 1.01788372e-01, 9.18946542e-02, 2.30353084e-02,
       1.02584502e-01, 1.80158771e-01, 1.87531530e-02, 1.96761622e-01,
       1.11744505e-01, 1.18633978e-03, 1.53076817e-03, 2.59736865e-01,
       3.71401092e-01, 2.20064943e-02, 5.65828781e-01, 2.59959657e-02,
       5.82864836e-01, 7.37699063e-02, 5.46040881e-02, 7.11503570e-02,
       6.22476276e-02, 6.76780428e-03, 3.38328916e-01, 1.79466370e-02,
       3.55684590e-01, 2.52160690e-01, 4.75278624e-03, 9.02476430e-04,
       3.20784546e-02, 1.09778329e-02, 2.66911378e-05, 9.46861513e-03,
       1.69684078e-01, 2.85964966e-02, 1.77022479e-02, 1.01254419e-01,
       1.87570635e-02, 3.10878978e-02, 1.53062038e-03, 2.02927837e-01,
       3.36609817e-01, 2.55579793e-01, 2.97477252e-02, 6.80416729e-01,
       1.19145507e-01, 3.41558986e-03, 6.23240447e-02, 3.51089838e-02,
       1.82767021e-01, 1.86477915e-01, 1.03135389e-01, 6.52019244e-01,
       1.89871820e-01, 1.06515940e-02, 3.85757099e-03, 6.90560102e-02,
       7.87288776e-02, 1.75708531e-01, 3.71441057e-02, 1.72562296e-01,
       6.04281653e-01, 1.88352463e-01, 3.07216078e-01, 3.72955329e-02,
       9.27430404e-01, 1.72053969e-02, 3.83384349e-02, 2.14113210e-02,
       7.97532844e-01, 4.61926669e-01, 4.78685754e-03, 7.35784169e-02,
       1.93633014e-01, 1.72771894e-01, 3.08593808e-01, 4.70050203e-01,
       2.25006025e-01, 4.41668390e-01, 1.42142678e-01, 7.25995408e-02,
       2.78217500e-02, 3.44766558e-01, 2.96054364e-03, 2.79457357e-02,
       8.27029124e-01, 3.00208474e-02, 7.08173425e-03, 4.94331409e-02,
       5.09298172e-02, 8.09542065e-03, 1.80679919e-01, 4.13991338e-03,
       2.22414917e-02, 7.10468946e-02, 1.72807360e-01, 2.87152699e-02,
       1.41539013e-01, 2.72905977e-01, 1.85627382e-01, 5.39230520e-01,
       4.00579140e-02, 7.51434420e-03, 7.66947757e-01, 1.05756058e-04,
       6.14460092e-02, 2.59459903e-01, 2.09635661e-01, 3.65073083e-02,
       3.60835922e-02, 1.34513906e-02, 2.76321528e-02, 1.02632878e-01,
       7.13643516e-02, 2.26633304e-02, 3.79165619e-01, 1.17660323e-02,
       1.86965506e-02, 4.59318002e-03, 8.64819365e-02, 8.67101410e-02,
       4.04618184e-01, 4.49936142e-03, 7.84642480e-01, 7.68614624e-03,
       2.46761987e-01, 1.40866995e-02, 1.68008182e-02, 2.00183295e-01,
       1.49818154e-02, 3.54096884e-01, 9.44301983e-02, 1.22997778e-02,
       1.96743140e-01, 1.26458928e-01, 1.37036145e-01, 1.89524973e-02,
       5.74193005e-03, 6.98309232e-04, 4.78148792e-01, 3.28780065e-02,
       8.83218768e-01, 9.96394938e-02, 1.69049802e-01, 1.63906582e-02,
       1.03257157e-01, 2.06723865e-02, 8.88703156e-03, 7.85571511e-04,
       8.83124268e-02, 1.04460705e-01, 1.20227722e-02, 2.28040233e-02,
       1.03376797e-01, 6.51861532e-02, 9.86891946e-02, 7.69415434e-01,
       1.90309445e-01, 1.89865136e-01, 4.65089982e-02, 9.50571913e-04,
       2.23330068e-03, 1.51754525e-02, 6.30147117e-02, 7.11843396e-02,
       1.16295019e-02, 4.82748530e-01, 8.36788764e-01, 8.60930877e-02,
       5.76152042e-02, 4.36987712e-03, 1.48032187e-01, 4.04810680e-02,
       3.61311150e-01, 6.34284158e-01, 9.18722792e-02, 7.53599472e-01,
       6.71373634e-02, 3.38652943e-03, 2.20739857e-01, 5.21066655e-02,
       1.08360491e-02, 3.75681795e-02, 8.36758922e-01, 1.64010928e-01,
       5.88963521e-02, 1.70336260e-01, 1.10723613e-02, 1.81442902e-01,
       1.89710786e-01, 7.56221733e-02, 2.46294538e-01, 1.80700127e-01,
       1.45563981e-01, 2.60651624e-02, 3.94900209e-01, 1.75729631e-01,
       4.47770145e-02, 6.51497035e-01, 4.81132112e-01, 1.50463180e-01,
       9.00398658e-02, 2.86333345e-02, 1.43790075e-01, 8.11070503e-02,
       7.86090184e-03, 2.75119334e-01, 2.30145959e-02, 3.04472682e-02,
       1.91670971e-01, 5.63515718e-01, 1.24625239e-02, 1.54863452e-01,
       4.99989984e-02, 1.45744978e-02, 2.29290260e-01, 4.06959980e-02,
       2.90471323e-03, 2.27623947e-01, 7.57521466e-03, 3.23914074e-03,
       2.01898018e-01, 5.22192289e-02, 2.25643547e-01, 3.61179436e-03,
       1.76107353e-02, 1.36551493e-03, 1.84413565e-01, 5.93987553e-01,
       1.12875140e-02, 3.48945581e-02, 1.68762983e-01, 2.35019134e-02,
       9.03907432e-02, 3.51703006e-01, 4.72577550e-02, 3.16199375e-01,
       4.72217627e-02, 5.65045216e-02, 1.34219019e-01, 1.45155868e-01,
       3.24385058e-01, 3.16144749e-01, 5.06443773e-01, 1.72662861e-02,
       4.88323743e-01, 1.76001303e-02, 3.31255886e-02, 1.13367615e-01,
       2.74673248e-02, 1.37015004e-01, 9.39300936e-03, 5.46158350e-01,
       3.12255295e-01, 4.68013760e-02, 4.78503471e-02, 6.30612667e-01,
       2.11039727e-01, 5.13981226e-02, 1.54494214e-01, 3.95704238e-03,
       2.13222733e-02, 1.57431349e-02, 5.93934876e-01, 1.98066436e-01,
       2.72647470e-01, 3.15238788e-01, 1.81009421e-01, 2.94403110e-01,
       1.69506884e-01])


plt.plot(fpr,tpr)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC CURVE')
plt.show()


# Decision Tree Model
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_auc_score,roc_curve
decision_tree_model = DecisionTreeClassifier(random_state=42)
decision_tree_model.fit(X_train_standardized, y_train)
y_pred_decision_tree = decision_tree_model.predict(X_test_standardized)
accuracy_dt = accuracy_score(y_test, y_pred_decision_tree)
print("Decision Tree Accuracy:", accuracy_dt)
print("Classification Report:\n", classification_report(y_test, y_pred_decision_tree))

Decision Tree Accuracy: 0.7728937728937729
Classification Report:
               precision    recall  f1-score   support

           0       0.86      0.87      0.87       229
           1       0.29      0.27      0.28        44

    accuracy                           0.77       273
   macro avg       0.57      0.57      0.57       273
weighted avg       0.77      0.77      0.77       273


pred=decision_tree_model.predict(X_test_standardized)


pred

array([1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
       0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 1, 0])


confusion_matrix(y_test,pred)

array([[199,  30],
       [ 32,  12]])


pd.crosstab(y_test,pred)


probability=decision_tree_model.predict_proba(X_test_standardized)[:,1]


probability

array([1., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.,
       1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 1., 0., 0.,
       0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0.,
       1., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,
       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1.,
       1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 1., 0., 1., 0., 0.,
       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0.,
       1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.,
       0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.,
       0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,
       0.])


# Roc_Curve
fpr,tpr,threshsholds = roc_curve(y_test,probability)


plt.plot(fpr,tpr)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC CURVE')
plt.show()


from sklearn import tree
plt.figure(figsize=(25,15))
tree.plot_tree(decision_tree_model,filled=True)

[Text(0.4673406862745098, 0.9642857142857143, 'x[21] <= 0.498\ngini = 0.279\nsamples = 1090\nvalue = [907, 183]'),
 Text(0.20147058823529412, 0.8928571428571429, 'x[27] <= -1.161\ngini = 0.196\nsamples = 788\nvalue = [701, 87]'),
 Text(0.06274509803921569, 0.8214285714285714, 'x[11] <= -0.34\ngini = 0.466\nsamples = 73\nvalue = [46, 27]'),
 Text(0.03137254901960784, 0.75, 'x[9] <= -0.214\ngini = 0.459\nsamples = 28\nvalue = [10, 18]'),
 Text(0.01568627450980392, 0.6785714285714286, 'x[30] <= -1.207\ngini = 0.142\nsamples = 13\nvalue = [1, 12]'),
 Text(0.00784313725490196, 0.6071428571428571, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.023529411764705882, 0.6071428571428571, 'gini = 0.0\nsamples = 12\nvalue = [0, 12]'),
 Text(0.047058823529411764, 0.6785714285714286, 'x[17] <= -0.915\ngini = 0.48\nsamples = 15\nvalue = [9, 6]'),
 Text(0.0392156862745098, 0.6071428571428571, 'x[17] <= -1.09\ngini = 0.375\nsamples = 8\nvalue = [2, 6]'),
 Text(0.03137254901960784, 0.5357142857142857, 'x[2] <= -0.167\ngini = 0.444\nsamples = 3\nvalue = [2, 1]'),
 Text(0.023529411764705882, 0.4642857142857143, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(0.0392156862745098, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.047058823529411764, 0.5357142857142857, 'gini = 0.0\nsamples = 5\nvalue = [0, 5]'),
 Text(0.054901960784313725, 0.6071428571428571, 'gini = 0.0\nsamples = 7\nvalue = [7, 0]'),
 Text(0.09411764705882353, 0.75, 'x[5] <= 0.591\ngini = 0.32\nsamples = 45\nvalue = [36, 9]'),
 Text(0.0784313725490196, 0.6785714285714286, 'x[11] <= 0.702\ngini = 0.25\nsamples = 41\nvalue = [35, 6]'),
 Text(0.07058823529411765, 0.6071428571428571, 'gini = 0.0\nsamples = 21\nvalue = [21, 0]'),
 Text(0.08627450980392157, 0.6071428571428571, 'x[2] <= -1.256\ngini = 0.42\nsamples = 20\nvalue = [14, 6]'),
 Text(0.0784313725490196, 0.5357142857142857, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(0.09411764705882353, 0.5357142857142857, 'x[29] <= -1.772\ngini = 0.291\nsamples = 17\nvalue = [14, 3]'),
 Text(0.08627450980392157, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.10196078431372549, 0.4642857142857143, 'x[11] <= 1.693\ngini = 0.219\nsamples = 16\nvalue = [14, 2]'),
 Text(0.09411764705882353, 0.39285714285714285, 'x[8] <= 1.396\ngini = 0.124\nsamples = 15\nvalue = [14, 1]'),
 Text(0.08627450980392157, 0.32142857142857145, 'gini = 0.0\nsamples = 14\nvalue = [14, 0]'),
 Text(0.10196078431372549, 0.32142857142857145, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.10980392156862745, 0.39285714285714285, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.10980392156862745, 0.6785714285714286, 'x[11] <= 0.156\ngini = 0.375\nsamples = 4\nvalue = [1, 3]'),
 Text(0.10196078431372549, 0.6071428571428571, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.11764705882352941, 0.6071428571428571, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(0.34019607843137256, 0.8214285714285714, 'x[15] <= -1.104\ngini = 0.154\nsamples = 715\nvalue = [655, 60]'),
 Text(0.2519607843137255, 0.75, 'x[26] <= -0.344\ngini = 0.296\nsamples = 144\nvalue = [118, 26]'),
 Text(0.19411764705882353, 0.6785714285714286, 'x[4] <= -0.076\ngini = 0.428\nsamples = 58\nvalue = [40, 18]'),
 Text(0.1568627450980392, 0.6071428571428571, 'x[18] <= 0.412\ngini = 0.278\nsamples = 36\nvalue = [30, 6]'),
 Text(0.14901960784313725, 0.5357142857142857, 'x[19] <= 0.346\ngini = 0.444\nsamples = 18\nvalue = [12, 6]'),
 Text(0.13333333333333333, 0.4642857142857143, 'x[8] <= -0.726\ngini = 0.278\nsamples = 12\nvalue = [10, 2]'),
 Text(0.12549019607843137, 0.39285714285714285, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.1411764705882353, 0.39285714285714285, 'gini = 0.0\nsamples = 10\nvalue = [10, 0]'),
 Text(0.16470588235294117, 0.4642857142857143, 'x[27] <= 0.695\ngini = 0.444\nsamples = 6\nvalue = [2, 4]'),
 Text(0.1568627450980392, 0.39285714285714285, 'gini = 0.0\nsamples = 4\nvalue = [0, 4]'),
 Text(0.17254901960784313, 0.39285714285714285, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(0.16470588235294117, 0.5357142857142857, 'gini = 0.0\nsamples = 18\nvalue = [18, 0]'),
 Text(0.23137254901960785, 0.6071428571428571, 'x[19] <= -0.057\ngini = 0.496\nsamples = 22\nvalue = [10, 12]'),
 Text(0.21176470588235294, 0.5357142857142857, 'x[31] <= -0.103\ngini = 0.426\nsamples = 13\nvalue = [9, 4]'),
 Text(0.19607843137254902, 0.4642857142857143, 'x[0] <= -0.746\ngini = 0.48\nsamples = 5\nvalue = [2, 3]'),
 Text(0.18823529411764706, 0.39285714285714285, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(0.20392156862745098, 0.39285714285714285, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(0.22745098039215686, 0.4642857142857143, 'x[6] <= 1.669\ngini = 0.219\nsamples = 8\nvalue = [7, 1]'),
 Text(0.2196078431372549, 0.39285714285714285, 'gini = 0.0\nsamples = 7\nvalue = [7, 0]'),
 Text(0.23529411764705882, 0.39285714285714285, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.25098039215686274, 0.5357142857142857, 'x[0] <= 1.597\ngini = 0.198\nsamples = 9\nvalue = [1, 8]'),
 Text(0.24313725490196078, 0.4642857142857143, 'gini = 0.0\nsamples = 8\nvalue = [0, 8]'),
 Text(0.25882352941176473, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.30980392156862746, 0.6785714285714286, 'x[11] <= 1.693\ngini = 0.169\nsamples = 86\nvalue = [78, 8]'),
 Text(0.30196078431372547, 0.6071428571428571, 'x[30] <= 2.527\ngini = 0.151\nsamples = 85\nvalue = [78, 7]'),
 Text(0.29411764705882354, 0.5357142857142857, 'x[29] <= -1.772\ngini = 0.133\nsamples = 84\nvalue = [78, 6]'),
 Text(0.27450980392156865, 0.4642857142857143, 'x[12] <= -0.313\ngini = 0.48\nsamples = 5\nvalue = [3, 2]'),
 Text(0.26666666666666666, 0.39285714285714285, 'x[22] <= -0.886\ngini = 0.444\nsamples = 3\nvalue = [1, 2]'),
 Text(0.25882352941176473, 0.32142857142857145, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.27450980392156865, 0.32142857142857145, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.2823529411764706, 0.39285714285714285, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(0.3137254901960784, 0.4642857142857143, 'x[14] <= 1.198\ngini = 0.096\nsamples = 79\nvalue = [75, 4]'),
 Text(0.2980392156862745, 0.39285714285714285, 'x[18] <= -0.691\ngini = 0.075\nsamples = 77\nvalue = [74, 3]'),
 Text(0.2901960784313726, 0.32142857142857145, 'x[18] <= -0.7\ngini = 0.227\nsamples = 23\nvalue = [20, 3]'),
 Text(0.2823529411764706, 0.25, 'x[33] <= 0.849\ngini = 0.165\nsamples = 22\nvalue = [20, 2]'),
 Text(0.27450980392156865, 0.17857142857142858, 'gini = 0.0\nsamples = 16\nvalue = [16, 0]'),
 Text(0.2901960784313726, 0.17857142857142858, 'x[18] <= -1.313\ngini = 0.444\nsamples = 6\nvalue = [4, 2]'),
 Text(0.2823529411764706, 0.10714285714285714, 'gini = 0.0\nsamples = 4\nvalue = [4, 0]'),
 Text(0.2980392156862745, 0.10714285714285714, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.2980392156862745, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.3058823529411765, 0.32142857142857145, 'gini = 0.0\nsamples = 54\nvalue = [54, 0]'),
 Text(0.32941176470588235, 0.39285714285714285, 'x[6] <= 0.926\ngini = 0.5\nsamples = 2\nvalue = [1, 1]'),
 Text(0.3215686274509804, 0.32142857142857145, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.33725490196078434, 0.32142857142857145, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.30980392156862746, 0.5357142857142857, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.3176470588235294, 0.6071428571428571, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.4284313725490196, 0.75, 'x[26] <= -0.344\ngini = 0.112\nsamples = 571\nvalue = [537, 34]'),
 Text(0.35784313725490197, 0.6785714285714286, 'x[12] <= -1.722\ngini = 0.18\nsamples = 230\nvalue = [207, 23]'),
 Text(0.3333333333333333, 0.6071428571428571, 'x[2] <= 0.387\ngini = 0.486\nsamples = 12\nvalue = [7, 5]'),
 Text(0.3254901960784314, 0.5357142857142857, 'gini = 0.0\nsamples = 7\nvalue = [7, 0]'),
 Text(0.3411764705882353, 0.5357142857142857, 'gini = 0.0\nsamples = 5\nvalue = [0, 5]'),
 Text(0.38235294117647056, 0.6071428571428571, 'x[2] <= -1.715\ngini = 0.152\nsamples = 218\nvalue = [200, 18]'),
 Text(0.3568627450980392, 0.5357142857142857, 'x[24] <= -0.632\ngini = 0.444\nsamples = 3\nvalue = [1, 2]'),
 Text(0.34901960784313724, 0.4642857142857143, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.36470588235294116, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.40784313725490196, 0.5357142857142857, 'x[0] <= -0.518\ngini = 0.138\nsamples = 215\nvalue = [199, 16]'),
 Text(0.3803921568627451, 0.4642857142857143, 'x[30] <= -0.768\ngini = 0.27\nsamples = 56\nvalue = [47, 9]'),
 Text(0.36470588235294116, 0.39285714285714285, 'x[19] <= 0.749\ngini = 0.444\nsamples = 9\nvalue = [3, 6]'),
 Text(0.3568627450980392, 0.32142857142857145, 'x[13] <= 0.592\ngini = 0.375\nsamples = 4\nvalue = [3, 1]'),
 Text(0.34901960784313724, 0.25, 'gini = 0.0\nsamples = 3\nvalue = [3, 0]'),
 Text(0.36470588235294116, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.37254901960784315, 0.32142857142857145, 'gini = 0.0\nsamples = 5\nvalue = [0, 5]'),
 Text(0.396078431372549, 0.39285714285714285, 'x[32] <= 2.182\ngini = 0.12\nsamples = 47\nvalue = [44, 3]'),
 Text(0.38823529411764707, 0.32142857142857145, 'x[4] <= -0.949\ngini = 0.083\nsamples = 46\nvalue = [44, 2]'),
 Text(0.3803921568627451, 0.25, 'x[2] <= 0.368\ngini = 0.48\nsamples = 5\nvalue = [3, 2]'),
 Text(0.37254901960784315, 0.17857142857142858, 'gini = 0.0\nsamples = 3\nvalue = [3, 0]'),
 Text(0.38823529411764707, 0.17857142857142858, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.396078431372549, 0.25, 'gini = 0.0\nsamples = 41\nvalue = [41, 0]'),
 Text(0.403921568627451, 0.32142857142857145, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.43529411764705883, 0.4642857142857143, 'x[14] <= 0.386\ngini = 0.084\nsamples = 159\nvalue = [152, 7]'),
 Text(0.42745098039215684, 0.39285714285714285, 'gini = 0.0\nsamples = 95\nvalue = [95, 0]'),
 Text(0.44313725490196076, 0.39285714285714285, 'x[0] <= 1.768\ngini = 0.195\nsamples = 64\nvalue = [57, 7]'),
 Text(0.42745098039215684, 0.32142857142857145, 'x[2] <= 1.661\ngini = 0.126\nsamples = 59\nvalue = [55, 4]'),
 Text(0.4196078431372549, 0.25, 'x[17] <= 0.833\ngini = 0.098\nsamples = 58\nvalue = [55, 3]'),
 Text(0.403921568627451, 0.17857142857142858, 'x[4] <= 2.418\ngini = 0.037\nsamples = 53\nvalue = [52, 1]'),
 Text(0.396078431372549, 0.10714285714285714, 'gini = 0.0\nsamples = 51\nvalue = [51, 0]'),
 Text(0.4117647058823529, 0.10714285714285714, 'x[30] <= 0.769\ngini = 0.5\nsamples = 2\nvalue = [1, 1]'),
 Text(0.403921568627451, 0.03571428571428571, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.4196078431372549, 0.03571428571428571, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.43529411764705883, 0.17857142857142858, 'x[8] <= -0.142\ngini = 0.48\nsamples = 5\nvalue = [3, 2]'),
 Text(0.42745098039215684, 0.10714285714285714, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.44313725490196076, 0.10714285714285714, 'gini = 0.0\nsamples = 3\nvalue = [3, 0]'),
 Text(0.43529411764705883, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.4588235294117647, 0.32142857142857145, 'x[27] <= 0.386\ngini = 0.48\nsamples = 5\nvalue = [2, 3]'),
 Text(0.45098039215686275, 0.25, 'gini = 0.0\nsamples = 3\nvalue = [0, 3]'),
 Text(0.4666666666666667, 0.25, 'gini = 0.0\nsamples = 2\nvalue = [2, 0]'),
 Text(0.49901960784313726, 0.6785714285714286, 'x[4] <= -0.45\ngini = 0.062\nsamples = 341\nvalue = [330, 11]'),
 Text(0.49117647058823527, 0.6071428571428571, 'gini = 0.0\nsamples = 154\nvalue = [154, 0]'),
 Text(0.5068627450980392, 0.6071428571428571, 'x[6] <= -1.305\ngini = 0.111\nsamples = 187\nvalue = [176, 11]'),
 Text(0.47843137254901963, 0.5357142857142857, 'x[2] <= 0.042\ngini = 0.5\nsamples = 2\nvalue = [1, 1]'),
 Text(0.47058823529411764, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.48627450980392156, 0.4642857142857143, 'gini = 0.0\nsamples = 1\nvalue = [1, 0]'),
 Text(0.5352941176470588, 0.5357142857142857, 'x[17] <= 0.307\ngini = 0.102\nsamples = 185\nvalue = [175, 10]'),
 Text(0.5019607843137255, 0.4642857142857143, 'x[11] <= -1.579\ngini = 0.055\nsamples = 142\nvalue = [138, 4]'),
 Text(0.4823529411764706, 0.39285714285714285, 'x[17] <= -0.783\ngini = 0.444\nsamples = 6\nvalue = [4, 2]'),
 Text(0.4745098039215686, 0.32142857142857145, 'gini = 0.0\nsamples = 2\nvalue = [0, 2]'),
 Text(0.49019607843137253, 0.32142857142857145, 'gini = 0.0\nsamples = 4\nvalue = [4, 0]'),
 Text(0.5215686274509804, 0.39285714285714285, 'x[30] <= -0.988\ngini = 0.029\nsamples = 136\nvalue = [134, 2]'),
 Text(0.5058823529411764, 0.32142857142857145, 'x[9] <= -0.672\ngini = 0.32\nsamples = 5\nvalue = [4, 1]'),
 Text(0.4980392156862745, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.5137254901960784, 0.25, 'gini = 0.0\nsamples = 4\nvalue = [4, 0]'),
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 Text(0.984313725490196, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1]'),
 Text(0.9921568627450981, 0.32142857142857145, 'gini = 0.0\nsamples = 4\nvalue = [0, 4]')]


from sklearn.model_selection import GridSearchCV
parameter={
 'criterion':['gini','entropy'],
  'splitter':['best','random'],
  'max_depth':[1,2,3,4,5],
  'max_features':['auto', 'sqrt', 'log2']
}


grid_search=GridSearchCV(estimator=decision_tree_model,param_grid=parameter,cv=5,scoring="accuracy")


grid_search.fit(X_train_standardized,y_train)

/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.
  warnings.warn(

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': [1, 2, 3, 4, 5],
                         'max_features': ['auto', 'sqrt', 'log2'],
                         'splitter': ['best', 'random']},
             scoring='accuracy')

GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'criterion': ['gini', 'entropy'],
                         'max_depth': [1, 2, 3, 4, 5],
                         'max_features': ['auto', 'sqrt', 'log2'],
                         'splitter': ['best', 'random']},
             scoring='accuracy')

DecisionTreeClassifier(random_state=42)

DecisionTreeClassifier(random_state=42)


grid_search.best_params_

{'criterion': 'gini',
 'max_depth': 1,
 'max_features': 'auto',
 'splitter': 'best'}


decision_tree_model_cv=DecisionTreeClassifier(criterion= 'entropy',
 max_depth=3,
 max_features='sqrt',
 splitter='best')
decision_tree_model_cv.fit(X_train_standardized,y_train)

DecisionTreeClassifier(criterion='entropy', max_depth=3, max_features='sqrt')

DecisionTreeClassifier(criterion='entropy', max_depth=3, max_features='sqrt')


pred=decision_tree_model_cv.predict(X_test_standardized)


print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.86      0.96      0.91       229
           1       0.47      0.18      0.26        44

    accuracy                           0.84       273
   macro avg       0.66      0.57      0.58       273
weighted avg       0.80      0.84      0.80       273


# Random Forest Model
from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier()


forest_params = [{'max_depth': list(range(10, 15)), 'max_features': list(range(0,14))}]


rfc_cv= GridSearchCV(rfc,param_grid=forest_params,cv=10,scoring="accuracy")


rfc_cv.fit(X_train_standardized,y_train)

/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: 
50 fits failed out of a total of 700.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
50 fits failed with the following error:
Traceback (most recent call last):
  File "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py", line 686, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_forest.py", line 340, in fit
    self._validate_params()
  File "/usr/local/lib/python3.10/dist-packages/sklearn/base.py", line 600, in _validate_params
    validate_parameter_constraints(
  File "/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py", line 97, in validate_parameter_constraints
    raise InvalidParameterError(
sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'auto' (deprecated), 'sqrt', 'log2'} or None. Got 0 instead.

  warnings.warn(some_fits_failed_message, FitFailedWarning)
/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan 0.83853211 0.84311927 0.84587156 0.84862385 0.84587156
 0.85229358 0.84678899 0.85321101 0.85412844 0.85412844 0.84770642
 0.85045872 0.85229358        nan 0.83761468 0.84587156 0.84678899
 0.85321101 0.85137615 0.85321101 0.85688073 0.85229358 0.84770642
 0.85045872 0.84862385 0.84954128 0.84954128        nan 0.83486239
 0.8412844  0.85229358 0.84678899 0.85045872 0.84862385 0.84770642
 0.85229358 0.85137615 0.85412844 0.85137615 0.85321101 0.85045872
        nan 0.84036697 0.84495413 0.84678899 0.85045872 0.85321101
 0.84587156 0.84862385 0.85137615 0.84770642 0.85321101 0.85412844
 0.85321101 0.85229358        nan 0.83944954 0.84587156 0.84678899
 0.85137615 0.84862385 0.85412844 0.85779817 0.85229358 0.85137615
 0.85229358 0.85045872 0.85045872 0.84678899]
  warnings.warn(

GridSearchCV(cv=10, estimator=RandomForestClassifier(),
             param_grid=[{'max_depth': [10, 11, 12, 13, 14],
                          'max_features': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                                           12, 13]}],
             scoring='accuracy')

GridSearchCV(cv=10, estimator=RandomForestClassifier(),
             param_grid=[{'max_depth': [10, 11, 12, 13, 14],
                          'max_features': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
                                           12, 13]}],
             scoring='accuracy')

RandomForestClassifier()

RandomForestClassifier()


pred=rfc_cv.predict(X_test_standardized)


print(classification_report(y_test,pred))

              precision    recall  f1-score   support

           0       0.86      0.99      0.92       229
           1       0.73      0.18      0.29        44

    accuracy                           0.86       273
   macro avg       0.79      0.58      0.61       273
weighted avg       0.84      0.86      0.82       273


rfc_cv.best_params_

{'max_depth': 14, 'max_features': 7}


# Combined Performance Metrics Calculations
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

# Calculate performance metrics for Logistic Regression
accuracy_lr = accuracy_score(y_test, y_pred_logistic_regression)
precision_lr = precision_score(y_test, y_pred_logistic_regression)
recall_lr = recall_score(y_test, y_pred_logistic_regression)
f1_score_lr = f1_score(y_test, y_pred_logistic_regression)
confusion_matrix_lr = confusion_matrix(y_test, y_pred_logistic_regression)

# Calculate performance metrics for Decision Tree
accuracy_dt = accuracy_score(y_test, y_pred_decision_tree)
precision_dt = precision_score(y_test, y_pred_decision_tree)
recall_dt = recall_score(y_test, y_pred_decision_tree)
f1_score_dt = f1_score(y_test, y_pred_decision_tree)
confusion_matrix_dt = confusion_matrix(y_test, y_pred_decision_tree)

# Calculate performance metrics for Random Forest
accuracy_rf = accuracy_score(y_test, pred)
precision_rf = precision_score(y_test, pred)
recall_rf = recall_score(y_test, pred)
f1_score_rf = f1_score(y_test, pred)
confusion_matrix_rf = confusion_matrix(y_test, pred)

# Print the performance metrics for each model
print("Logistic Regression Performance Metrics:")
print(f"Accuracy: {accuracy_lr:.2f}")
print(f"Precision: {precision_lr:.2f}")
print(f"Recall: {recall_lr:.2f}")
print(f"F1 Score: {f1_score_lr:.2f}")
print("Confusion Matrix:")
print(confusion_matrix_lr)

print("\nDecision Tree Performance Metrics:")
print(f"Accuracy: {accuracy_dt:.2f}")
print(f"Precision: {precision_dt:.2f}")
print(f"Recall: {recall_dt:.2f}")
print(f"F1 Score: {f1_score_dt:.2f}")
print("Confusion Matrix:")
print(confusion_matrix_dt)

print("\nRandom Forest Performance Metrics:")
print(f"Accuracy: {accuracy_rf:.2f}")
print(f"Precision: {precision_rf:.2f}")
print(f"Recall: {recall_rf:.2f}")
print(f"F1 Score: {f1_score_rf:.2f}")
print("Confusion Matrix:")
print(confusion_matrix_rf)

Logistic Regression Performance Metrics:
Accuracy: 0.88
Precision: 0.71
Recall: 0.39
F1 Score: 0.50
Confusion Matrix:
[[222   7]
 [ 27  17]]

Decision Tree Performance Metrics:
Accuracy: 0.77
Precision: 0.29
Recall: 0.27
F1 Score: 0.28
Confusion Matrix:
[[199  30]
 [ 32  12]]

Random Forest Performance Metrics:
Accuracy: 0.86
Precision: 0.73
Recall: 0.18
F1 Score: 0.29
Confusion Matrix:
[[226   3]
 [ 36   8]]

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
count	1470.000000	1470.000000	1470.000000	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	...	1470.000000	1470.0	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000	1470.000000
mean	36.923810	802.485714	9.192517	2.912925	1.0	1024.865306	2.721769	65.891156	2.729932	2.063946	...	2.712245	80.0	0.793878	11.279592	2.799320	2.761224	7.008163	4.229252	2.187755	4.123129
std	9.135373	403.509100	8.106864	1.024165	0.0	602.024335	1.093082	20.329428	0.711561	1.106940	...	1.081209	0.0	0.852077	7.780782	1.289271	0.706476	6.126525	3.623137	3.222430	3.568136
min	18.000000	102.000000	1.000000	1.000000	1.0	1.000000	1.000000	30.000000	1.000000	1.000000	...	1.000000	80.0	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	465.000000	2.000000	2.000000	1.0	491.250000	2.000000	48.000000	2.000000	1.000000	...	2.000000	80.0	0.000000	6.000000	2.000000	2.000000	3.000000	2.000000	0.000000	2.000000
50%	36.000000	802.000000	7.000000	3.000000	1.0	1020.500000	3.000000	66.000000	3.000000	2.000000	...	3.000000	80.0	1.000000	10.000000	3.000000	3.000000	5.000000	3.000000	1.000000	3.000000
75%	43.000000	1157.000000	14.000000	4.000000	1.0	1555.750000	4.000000	83.750000	3.000000	3.000000	...	4.000000	80.0	1.000000	15.000000	3.000000	3.000000	9.000000	7.000000	3.000000	7.000000
max	60.000000	1499.000000	29.000000	5.000000	1.0	2068.000000	4.000000	100.000000	4.000000	5.000000	...	4.000000	80.0	3.000000	40.000000	6.000000	4.000000	40.000000	18.000000	15.000000	17.000000

	Age	DailyRate	DistanceFromHome	Education	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	HourlyRate	JobInvolvement	JobLevel	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
Age	1.000000	0.010661	-0.001686	0.208034	NaN	-0.010145	0.010146	0.024287	0.029820	0.509604	...	0.053535	NaN	0.037510	0.680381	-0.019621	-0.021490	0.311309	0.212901	0.216513	0.202089
DailyRate	0.010661	1.000000	-0.004985	-0.016806	NaN	-0.050990	0.018355	0.023381	0.046135	0.002966	...	0.007846	NaN	0.042143	0.014515	0.002453	-0.037848	-0.034055	0.009932	-0.033229	-0.026363
DistanceFromHome	-0.001686	-0.004985	1.000000	0.021042	NaN	0.032916	-0.016075	0.031131	0.008783	0.005303	...	0.006557	NaN	0.044872	0.004628	-0.036942	-0.026556	0.009508	0.018845	0.010029	0.014406
Education	0.208034	-0.016806	0.021042	1.000000	NaN	0.042070	-0.027128	0.016775	0.042438	0.101589	...	-0.009118	NaN	0.018422	0.148280	-0.025100	0.009819	0.069114	0.060236	0.054254	0.069065
EmployeeCount	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
EmployeeNumber	-0.010145	-0.050990	0.032916	0.042070	NaN	1.000000	0.017621	0.035179	-0.006888	-0.018519	...	-0.069861	NaN	0.062227	-0.014365	0.023603	0.010309	-0.011240	-0.008416	-0.009019	-0.009197
EnvironmentSatisfaction	0.010146	0.018355	-0.016075	-0.027128	NaN	0.017621	1.000000	-0.049857	-0.008278	0.001212	...	0.007665	NaN	0.003432	-0.002693	-0.019359	0.027627	0.001458	0.018007	0.016194	-0.004999
HourlyRate	0.024287	0.023381	0.031131	0.016775	NaN	0.035179	-0.049857	1.000000	0.042861	-0.027853	...	0.001330	NaN	0.050263	-0.002334	-0.008548	-0.004607	-0.019582	-0.024106	-0.026716	-0.020123
JobInvolvement	0.029820	0.046135	0.008783	0.042438	NaN	-0.006888	-0.008278	0.042861	1.000000	-0.012630	...	0.034297	NaN	0.021523	-0.005533	-0.015338	-0.014617	-0.021355	0.008717	-0.024184	0.025976
JobLevel	0.509604	0.002966	0.005303	0.101589	NaN	-0.018519	0.001212	-0.027853	-0.012630	1.000000	...	0.021642	NaN	0.013984	0.782208	-0.018191	0.037818	0.534739	0.389447	0.353885	0.375281
JobSatisfaction	-0.004892	0.030571	-0.003669	-0.011296	NaN	-0.046247	-0.006784	-0.071335	-0.021476	-0.001944	...	-0.012454	NaN	0.010690	-0.020185	-0.005779	-0.019459	-0.003803	-0.002305	-0.018214	-0.027656
MonthlyIncome	0.497855	0.007707	-0.017014	0.094961	NaN	-0.014829	-0.006259	-0.015794	-0.015271	0.950300	...	0.025873	NaN	0.005408	0.772893	-0.021736	0.030683	0.514285	0.363818	0.344978	0.344079
MonthlyRate	0.028051	-0.032182	0.027473	-0.026084	NaN	0.012648	0.037600	-0.015297	-0.016322	0.039563	...	-0.004085	NaN	-0.034323	0.026442	0.001467	0.007963	-0.023655	-0.012815	0.001567	-0.036746
NumCompaniesWorked	0.299635	0.038153	-0.029251	0.126317	NaN	-0.001251	0.012594	0.022157	0.015012	0.142501	...	0.052733	NaN	0.030075	0.237639	-0.066054	-0.008366	-0.118421	-0.090754	-0.036814	-0.110319
PercentSalaryHike	0.003634	0.022704	0.040235	-0.011111	NaN	-0.012944	-0.031701	-0.009062	-0.017205	-0.034730	...	-0.040490	NaN	0.007528	-0.020608	-0.005221	-0.003280	-0.035991	-0.001520	-0.022154	-0.011985
PerformanceRating	0.001904	0.000473	0.027110	-0.024539	NaN	-0.020359	-0.029548	-0.002172	-0.029071	-0.021222	...	-0.031351	NaN	0.003506	0.006744	-0.015579	0.002572	0.003435	0.034986	0.017896	0.022827
RelationshipSatisfaction	0.053535	0.007846	0.006557	-0.009118	NaN	-0.069861	0.007665	0.001330	0.034297	0.021642	...	1.000000	NaN	-0.045952	0.024054	0.002497	0.019604	0.019367	-0.015123	0.033493	-0.000867
StandardHours	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
StockOptionLevel	0.037510	0.042143	0.044872	0.018422	NaN	0.062227	0.003432	0.050263	0.021523	0.013984	...	-0.045952	NaN	1.000000	0.010136	0.011274	0.004129	0.015058	0.050818	0.014352	0.024698
TotalWorkingYears	0.680381	0.014515	0.004628	0.148280	NaN	-0.014365	-0.002693	-0.002334	-0.005533	0.782208	...	0.024054	NaN	0.010136	1.000000	-0.035662	0.001008	0.628133	0.460365	0.404858	0.459188
TrainingTimesLastYear	-0.019621	0.002453	-0.036942	-0.025100	NaN	0.023603	-0.019359	-0.008548	-0.015338	-0.018191	...	0.002497	NaN	0.011274	-0.035662	1.000000	0.028072	0.003569	-0.005738	-0.002067	-0.004096
WorkLifeBalance	-0.021490	-0.037848	-0.026556	0.009819	NaN	0.010309	0.027627	-0.004607	-0.014617	0.037818	...	0.019604	NaN	0.004129	0.001008	0.028072	1.000000	0.012089	0.049856	0.008941	0.002759
YearsAtCompany	0.311309	-0.034055	0.009508	0.069114	NaN	-0.011240	0.001458	-0.019582	-0.021355	0.534739	...	0.019367	NaN	0.015058	0.628133	0.003569	0.012089	1.000000	0.758754	0.618409	0.769212
YearsInCurrentRole	0.212901	0.009932	0.018845	0.060236	NaN	-0.008416	0.018007	-0.024106	0.008717	0.389447	...	-0.015123	NaN	0.050818	0.460365	-0.005738	0.049856	0.758754	1.000000	0.548056	0.714365
YearsSinceLastPromotion	0.216513	-0.033229	0.010029	0.054254	NaN	-0.009019	0.016194	-0.026716	-0.024184	0.353885	...	0.033493	NaN	0.014352	0.404858	-0.002067	0.008941	0.618409	0.548056	1.000000	0.510224
YearsWithCurrManager	0.202089	-0.026363	0.014406	0.069065	NaN	-0.009197	-0.004999	-0.020123	0.025976	0.375281	...	-0.000867	NaN	0.024698	0.459188	-0.004096	0.002759	0.769212	0.714365	0.510224	1.000000

	Age	Attrition	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
0	41	Yes	Travel_Rarely	1102	Sales	1	2	Life Sciences	1	1	...	1	80	0	8	0	1	6	4	0	5
1	49	No	Travel_Frequently	279	Research & Development	8	1	Life Sciences	1	2	...	4	80	1	10	3	3	10	7	1	7
2	37	Yes	Travel_Rarely	1373	Research & Development	2	2	Other	1	4	...	2	80	0	7	3	3	0	0	0	0
3	33	No	Travel_Frequently	1392	Research & Development	3	4	Life Sciences	1	5	...	3	80	0	8	3	3	8	7	3	0
4	27	No	Travel_Rarely	591	Research & Development	2	1	Medical	1	7	...	4	80	1	6	3	3	2	2	2	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1465	36	No	Travel_Frequently	884	Research & Development	23	2	Medical	1	2061	...	3	80	1	17	3	3	5	2	0	3
1466	39	No	Travel_Rarely	613	Research & Development	6	1	Medical	1	2062	...	1	80	1	9	5	3	7	7	1	7
1467	27	No	Travel_Rarely	155	Research & Development	4	3	Life Sciences	1	2064	...	2	80	1	6	0	3	6	2	0	3
1468	49	No	Travel_Frequently	1023	Sales	2	3	Medical	1	2065	...	4	80	0	17	3	2	9	6	0	8
1469	34	No	Travel_Rarely	628	Research & Development	8	3	Medical	1	2068	...	1	80	0	6	3	4	4	3	1	2

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
1085	31	1	561	1	3	2	1	1	1537	3	...	0	80	0	7	2	0	7	2	7	7
1393	27	2	954	2	9	2	2	1	1965	3	...	0	80	0	7	5	2	7	7	0	7
911	25	1	599	2	24	0	1	1	1273	2	...	3	80	0	1	4	2	1	0	1	0
1027	34	2	401	1	1	2	1	1	1447	3	...	0	80	1	7	2	1	5	4	0	2
56	35	1	853	2	18	4	1	1	74	1	...	3	80	1	9	3	1	9	8	1	8

	Age	BusinessTravel	DailyRate	Department	DistanceFromHome	Education	EducationField	EmployeeCount	EmployeeNumber	EnvironmentSatisfaction	...	RelationshipSatisfaction	StandardHours	StockOptionLevel	TotalWorkingYears	TrainingTimesLastYear	WorkLifeBalance	YearsAtCompany	YearsInCurrentRole	YearsSinceLastPromotion	YearsWithCurrManager
460	26	2	775	2	29	1	3	1	618	0	...	0	80	2	8	5	2	0	0	0	0
1006	49	1	1475	1	28	1	1	1	1420	0	...	0	80	0	20	2	2	4	3	1	3
843	26	2	1384	1	3	3	3	1	1177	0	...	1	80	1	8	2	2	8	7	0	7
486	37	2	558	2	2	2	2	1	656	3	...	2	80	1	17	3	1	3	0	1	0
461	35	2	195	2	1	2	3	1	620	0	...	3	80	0	5	3	2	5	4	0	3

col_0	0	1
Attrition
0	199	30
1	32	12